Anomaly detection by auto-association

Alexander Iversen, Nicholas K. Taylor, Keith E. Brown

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Anomaly detectors (or novelty detectors) are systems for detecting behaviour that deviates from "normality", and are useful in a wide range of surveillance, monitoring and diagnosis applications. Feed-forward auto-associative neural networks have, in several studies, shown to be effective anomaly detectors although they have a tendency to produce false negatives. Existing methods rely on anomalous examples (counter-examples) during training to prevent this problem. However, counter-examples may be hard to obtain in practical anomaly detection scenarios. We therefore propose a training scheme based on regularisation, which both reduces the problem of false negatives and also speeds up the training process, without relying on counter-examples. Experimental results on benchmark machine learning problems verify the potential of the proposed approach. © 2006 IEEE.

Original languageEnglish
Title of host publicationProceedings of the 7th Nordic Signal Processing Symposium, NORSIG 2006
Place of PublicationNEW YORK
Number of pages4
ISBN (Print)1424404126, 9781424404124
Publication statusPublished - 2006
Event7th Nordic Signal Processing Symposium 2006 - Reykjavik, Iceland
Duration: 7 Jun 20069 Jun 2006


Conference7th Nordic Signal Processing Symposium 2006
Abbreviated titleNORSIG 2006


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